fitVsDatCorrelation=0.700098429487307 cont.fitVsDatCorrelation=0.243666622789987 fstatistic=15734.7400561834,49,623 cont.fstatistic=8523.11889107755,49,623 residuals=-0.328309383917952,-0.0655260590389723,-0.00678335849588052,0.0618326576664306,0.724822790371101 cont.residuals=-0.409187841025343,-0.0990425817931293,-0.0222025309212730,0.0763563645071993,0.933159043143462 predictedValues: Include Exclude Both Lung 48.1967019273722 50.4903172613319 50.1454221005777 cerebhem 55.4543149347375 48.6132989129122 51.5387121526916 cortex 48.7270738697867 46.9658956381365 46.6150965049557 heart 50.1633243395027 46.4727676281365 48.9123289384819 kidney 48.4312738532883 45.0139497753282 48.1862975126168 liver 54.1219134229082 46.9701565244068 51.8494523067107 stomach 52.4883045211124 49.1174986857874 52.3581443210546 testicle 52.8687912771775 47.7232522981413 48.9089458916372 diffExp=-2.29361533395976,6.84101602182537,1.76117823165016,3.69055671136619,3.41732407796010,7.15175689850145,3.370805835325,5.14553897903613 diffExpScore=1.11923825704607 diffExp1.5=0,0,0,0,0,0,0,0 diffExp1.5Score=0 diffExp1.4=0,0,0,0,0,0,0,0 diffExp1.4Score=0 diffExp1.3=0,0,0,0,0,0,0,0 diffExp1.3Score=0 diffExp1.2=0,0,0,0,0,0,0,0 diffExp1.2Score=0 cont.predictedValues: Include Exclude Both Lung 47.5213211308851 48.9229546403507 46.0769753633917 cerebhem 47.8696453008368 48.9318663515137 47.1415280960932 cortex 46.3892959274652 51.2113219864824 48.6953557270171 heart 47.1288768586102 46.7220592314817 47.629600999842 kidney 49.4921454738914 47.8218452621667 48.4645974968143 liver 48.928889662986 49.0829851054408 52.1599457486736 stomach 46.9105081198947 48.9347746257485 49.1912411651562 testicle 49.2750720411704 47.9916911242361 47.4959252895796 cont.diffExp=-1.40163350946565,-1.06222105067695,-4.82202605901728,0.406817627128540,1.67030021172469,-0.154095442454881,-2.02426650585387,1.28338091693439 cont.diffExpScore=1.80534963861843 cont.diffExp1.5=0,0,0,0,0,0,0,0 cont.diffExp1.5Score=0 cont.diffExp1.4=0,0,0,0,0,0,0,0 cont.diffExp1.4Score=0 cont.diffExp1.3=0,0,0,0,0,0,0,0 cont.diffExp1.3Score=0 cont.diffExp1.2=0,0,0,0,0,0,0,0 cont.diffExp1.2Score=0 tran.correlation=0.172912545433263 cont.tran.correlation=-0.432437194118895 tran.covariance=0.000356480092892767 cont.tran.covariance=-0.000274301060479074 tran.mean=49.4886771793791 cont.tran.mean=48.3209533026975 weightedLogRatios: wLogRatio Lung -0.181246788702077 cerebhem 0.520030686815049 cortex 0.142386723922839 heart 0.296277064222770 kidney 0.281245988685598 liver 0.55562202379088 stomach 0.260681419222034 testicle 0.401039774735131 cont.weightedLogRatios: wLogRatio Lung -0.112660105568500 cerebhem -0.0851433896058073 cortex -0.38434481727983 heart 0.033365000350226 kidney 0.133365532826837 liver -0.0122379200824638 stomach -0.163467426871313 testicle 0.102506281883205 varWeightedLogRatios=0.0543423203123587 cont.varWeightedLogRatios=0.0277282607047935 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.98991255806881 0.054597825163335 73.0782324411746 8.79889498158488e-308 *** df.mm.trans1 -0.0383789398874949 0.0459880992089718 -0.83454068656109 0.404296257193487 df.mm.trans2 -0.0568411882847467 0.0423774265892682 -1.34130816473743 0.180309174236767 df.mm.exp2 0.0749784951622557 0.0553440715112655 1.35477013372595 0.175981874917148 df.mm.exp3 0.0115871244633223 0.0553440715112655 0.209365233654046 0.834231572331535 df.mm.exp4 -0.0180237711270166 0.0553440715112655 -0.325667603319495 0.744785208092062 df.mm.exp5 -0.070101449246861 0.0553440715112655 -1.26664785102758 0.205754580672760 df.mm.exp6 0.0102622703839538 0.0553440715112655 0.185426733229500 0.852954693682965 df.mm.exp7 0.0145532857714875 0.0553440715112655 0.262960157684191 0.792668226796195 df.mm.exp8 0.0611266858172974 0.0553440715112655 1.10448480113818 0.269809279482120 df.mm.trans1:exp2 0.0652904383113623 0.048308266089033 1.35153760623556 0.177013808939636 df.mm.trans2:exp2 -0.112862942003057 0.040055098222493 -2.81769230413918 0.00499042753282713 ** df.mm.trans1:exp3 -0.000642911201874476 0.048308266089033 -0.0133085133026629 0.989385916775896 df.mm.trans2:exp3 -0.0839469913647861 0.0400550982224930 -2.09578792937888 0.0365039458409388 * df.mm.trans1:exp4 0.05801734599443 0.048308266089033 1.20098175098032 0.230214680781016 df.mm.trans2:exp4 -0.0648913107388176 0.040055098222493 -1.62005121990633 0.105727306886127 df.mm.trans1:exp5 0.0749566143232382 0.048308266089033 1.55163122984153 0.121258308318115 df.mm.trans2:exp5 -0.0447076945120325 0.0400550982224930 -1.11615490901298 0.264786140951991 df.mm.trans1:exp6 0.105686293511964 0.048308266089033 2.18774760653140 0.0290588216013461 * df.mm.trans2:exp6 -0.0825314184149721 0.040055098222493 -2.06044728579960 0.0397704538320013 * df.mm.trans1:exp7 0.0707464940103493 0.048308266089033 1.4644800929092 0.143567233274515 df.mm.trans2:exp7 -0.0421195062477924 0.040055098222493 -1.05153920766421 0.293418705652169 df.mm.trans1:exp8 0.0313959280285509 0.048308266089033 0.649907988224783 0.515991218573251 df.mm.trans2:exp8 -0.117489517632962 0.040055098222493 -2.93319759148625 0.00347820209296004 ** df.mm.trans1:probe2 -0.0324826348691206 0.0330744088136565 -0.982107799783512 0.326427978172222 df.mm.trans1:probe3 -0.150169864735290 0.0330744088136565 -4.54036429135763 6.74244311040226e-06 *** df.mm.trans1:probe4 -0.177079796677619 0.0330744088136565 -5.3539822185575 1.21138778132418e-07 *** df.mm.trans1:probe5 -0.155599076668739 0.0330744088136565 -4.70451573436714 3.13680148188885e-06 *** df.mm.trans1:probe6 -0.112257817965486 0.0330744088136565 -3.39409900258398 0.000732249322973395 *** df.mm.trans1:probe7 -0.150352833841084 0.0330744088136565 -4.54589633599142 6.57328176166554e-06 *** df.mm.trans1:probe8 -0.207072747072333 0.0330744088136565 -6.26081476585099 7.13367008471764e-10 *** df.mm.trans1:probe9 -0.122551202064539 0.0330744088136566 -3.70531799237896 0.000229901370905637 *** df.mm.trans1:probe10 -0.219390554279620 0.0330744088136566 -6.63324189755534 7.13463441736729e-11 *** df.mm.trans1:probe11 -0.151502778721607 0.0330744088136565 -4.58066475428735 5.59969701023913e-06 *** df.mm.trans1:probe12 -0.198887225706036 0.0330744088136566 -6.01332670302833 3.09754101521217e-09 *** df.mm.trans2:probe2 -0.0647205654870512 0.0330744088136565 -1.95681700167919 0.0508152459983914 . df.mm.trans2:probe3 -0.0665189313946257 0.0330744088136565 -2.01119033659522 0.0447355938105905 * df.mm.trans2:probe4 0.0241309835391722 0.0330744088136565 0.729596821371102 0.465910883507386 df.mm.trans2:probe5 0.0295991825541185 0.0330744088136565 0.894927033189988 0.371171711807175 df.mm.trans2:probe6 -0.103127297788947 0.0330744088136565 -3.11803903646391 0.00190451689219838 ** df.mm.trans3:probe2 -0.0604358381148017 0.0330744088136565 -1.82726888499508 0.0681375759070303 . df.mm.trans3:probe3 -0.120135508006466 0.0330744088136565 -3.63227982949954 0.000304055898809514 *** df.mm.trans3:probe4 -0.0520403775830547 0.0330744088136565 -1.57343334165861 0.116126185827333 df.mm.trans3:probe5 0.0565455608990871 0.0330744088136565 1.70964691213889 0.0878291373362413 . df.mm.trans3:probe6 0.129798466998210 0.0330744088136565 3.92443800672306 9.66140266604764e-05 *** df.mm.trans3:probe7 -0.062522621376682 0.0330744088136565 -1.89036247719313 0.0591733525063787 . df.mm.trans3:probe8 0.389739256396412 0.0330744088136565 11.7837104388541 4.47468858326188e-29 *** df.mm.trans3:probe9 -0.022956620269875 0.0330744088136565 -0.694090116597825 0.487884423194099 df.mm.trans3:probe10 -0.0556443568559671 0.0330744088136566 -1.68239913733519 0.0929925458168372 . cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.89326582509597 0.0741579239144634 52.4996604487824 6.39057017694893e-231 *** df.mm.trans1 -0.0382882242937983 0.0624636961620214 -0.612967637945798 0.54012143169903 df.mm.trans2 -0.00608065897907519 0.0575594717792555 -0.105641326980813 0.915900938351434 df.mm.exp2 -0.0153556787045725 0.0751715188649164 -0.204275222004849 0.838205144777106 df.mm.exp3 -0.0336660862560236 0.0751715188649164 -0.447856937898538 0.65441213897265 df.mm.exp4 -0.0874639214065785 0.0751715188649164 -1.16352473286793 0.245062096302989 df.mm.exp5 -0.0326488614533583 0.0751715188649164 -0.434324887222626 0.664202914589801 df.mm.exp6 -0.0915462328356833 0.0751715188649164 -1.21783135711535 0.223749100932109 df.mm.exp7 -0.0780974043947549 0.0751715188649164 -1.03892279381898 0.299243682865071 df.mm.exp8 -0.0133095217891408 0.0751715188649164 -0.177055379352625 0.859522435556528 df.mm.trans1:exp2 0.022658796204083 0.0656150810101486 0.345329089825834 0.729963543045847 df.mm.trans2:exp2 0.0155378201892187 0.0544051510748592 0.285594652018139 0.775283527075414 df.mm.trans1:exp3 0.00955635141270021 0.0656150810101486 0.14564260632738 0.884250640341331 df.mm.trans2:exp3 0.0793800200179969 0.0544051510748592 1.45905338832298 0.145054404544265 df.mm.trans1:exp4 0.079171355103881 0.0656150810101486 1.20660302304032 0.228043030036540 df.mm.trans2:exp4 0.0414336285857582 0.0544051510748592 0.761575471571566 0.446601606539362 df.mm.trans1:exp5 0.073284364898508 0.0656150810101486 1.11688294474822 0.264474929205867 df.mm.trans2:exp5 0.00988470401882868 0.0544051510748592 0.181686914263463 0.85588753259663 df.mm.trans1:exp6 0.120735769278728 0.0656150810101486 1.84006126975678 0.0662347525514624 . df.mm.trans2:exp6 0.0948119656623341 0.0544051510748592 1.74270200135786 0.0818790886895956 . df.mm.trans1:exp7 0.0651606322359998 0.0656150810101486 0.993074019460884 0.321059426650933 df.mm.trans2:exp7 0.0783389792921883 0.0544051510748592 1.43991842214347 0.150392777191313 df.mm.trans1:exp8 0.0495493606057971 0.0656150810101486 0.755152014490897 0.450443159115174 df.mm.trans2:exp8 -0.00590929025096673 0.0544051510748592 -0.10861637426272 0.913541745668001 df.mm.trans1:probe2 0.0247228529505515 0.0449235749772341 0.550331378637616 0.582289364439049 df.mm.trans1:probe3 -0.0289904873040939 0.0449235749772341 -0.64532903534025 0.518951565295452 df.mm.trans1:probe4 0.0320921212177460 0.0449235749772341 0.714371490559451 0.475265065707012 df.mm.trans1:probe5 0.0149495281939318 0.0449235749772341 0.332776903919773 0.73941464134987 df.mm.trans1:probe6 0.0474677107613977 0.0449235749772341 1.05663253170419 0.291088879802841 df.mm.trans1:probe7 -0.000368081440443291 0.0449235749772341 -0.00819350286859012 0.993465226652448 df.mm.trans1:probe8 0.0154771761116390 0.0449235749772341 0.34452236090922 0.730569748136482 df.mm.trans1:probe9 -0.0152503040367863 0.0449235749772341 -0.339472182356695 0.734368454524766 df.mm.trans1:probe10 -0.0278535011339427 0.0449235749772341 -0.620019692289806 0.535471531118398 df.mm.trans1:probe11 0.0126700892713474 0.0449235749772341 0.282036531548708 0.778009162651965 df.mm.trans1:probe12 0.0615021551244974 0.0449235749772341 1.36903964467799 0.171480248645687 df.mm.trans2:probe2 0.0102256387876820 0.0449235749772341 0.227622997343023 0.82001402001891 df.mm.trans2:probe3 0.0248223217690682 0.0449235749772341 0.552545557241324 0.58077281198678 df.mm.trans2:probe4 0.0346008134920270 0.0449235749772341 0.770215048770308 0.441464297614233 df.mm.trans2:probe5 -0.0145719066350624 0.0449235749772341 -0.324371037755721 0.745766021659654 df.mm.trans2:probe6 -0.00609222311156629 0.0449235749772341 -0.135613052047920 0.892170991978327 df.mm.trans3:probe2 -0.00101004065106694 0.0449235749772341 -0.0224835323452952 0.982069447077182 df.mm.trans3:probe3 -0.036110127748119 0.0449235749772341 -0.803812425133542 0.421811902433846 df.mm.trans3:probe4 0.0256067471533256 0.0449235749772341 0.570006887615297 0.568878516013737 df.mm.trans3:probe5 -0.00272561935933503 0.0449235749772341 -0.0606723610201613 0.951639601540313 df.mm.trans3:probe6 -0.0581342300795116 0.0449235749772341 -1.2940695416376 0.196120901670122 df.mm.trans3:probe7 -0.0438871706505584 0.0449235749772341 -0.976929611519099 0.328983186359032 df.mm.trans3:probe8 -0.0397556632040672 0.0449235749772341 -0.884962143467304 0.376518467602148 df.mm.trans3:probe9 -0.0321763136155702 0.0449235749772341 -0.716245615623339 0.474108088708062 df.mm.trans3:probe10 0.00235675410427150 0.0449235749772341 0.0524614104168207 0.958177859685822